Getting to Know Ourselves (Really)

How much do you really know about how you learn best? While most of us would like to think that we know ourselves pretty well, research shows that our recollections of how we go about learning something (or studying for a test or writing an essay) are actually quite poor.

This is an area in which learning analytics can be especially helpful by providing us with accurate data about what we do when learn online and whether or not how we are currently going about things is effective for us.

For example, in each of the images above, the computer could be tracking useful information to inform the essay-writer about her writing process. Keystroke and clickstream data can be used to determine when and for how long she is in periods of writing “flow”, how often she is switching applications to reference the assignment or online sources, and how frequently and for how long she goes off-task.

Information about how she organizes (and reorganizes) information in the essay itself can be generated by application of natural language processing to the text of the essay itself at various points in the process. While some learning analytics applications try to use such data to make automatic adjustments that “personalize” the learning experience, a potentially more powerful (and more empowering) approach is to show this data back to the learners themselves to help them better understand how they work and how this can be improved.

« By using learning analytics to better inform ourselves about our learning patterns we can become “smarter” learners »

For example, the analytics described above could help the student in the images manage her time better for future essays and provide input into which parts of the essay she should focus on in her edits. This approach follows from extensive research in the educational literature which shows that learners are more successful when they are intentional and reflective about their efforts to learn.

The use of self-tracking tools to better understand our daily behavior patterns has already become popular in personal domains such as exercise (e.g. Fitbit), sleep (e.g. Beddit) and even our emotions (e.g. Moodtracker). By using learning analytics to better inform ourselves about our learning patterns we can become more effective — and thus “smarter” — learners. By using learning analytics to better inform ourselves about our learning patterns we can become more effective — and thus “smarter” — learners.

One comment on “Getting to Know Ourselves (Really)”

Imagine the power of implementing learning analytics into k-12 schooling and higher education. If we can understand how to teach better and how to learn more effectively, the potential is limitless. We could have students graduating college earlier, with better skills, and retaining more information when they’re entering the workforce. It’s an exciting time to be alive!

Every day we generate a huge amount of big data, but we need to resort to analytics to make abstract information meaningful and get valuable knowledge from it. In education, learning platforms let us easily gather an immense quantity of data regarding students’ behaviour, interactions, preferences and opinions. When properly analysed — through learning analytics — all these data might provide useful insight on how to make learning processes more adaptive, attractive and efficient.

Are these techniques allowing us to provide better support to our students? Are we taking advantage of big data and analytics to help shape the citizens of the future?